Toward combining qualitative race-specific and quantitative race-nonspecific disease resistance by genomic selection

Key message A novel genomic selection strategy offers the unique opportunity to develop qualitative race-specific resistant varieties that possess high levels of the more durable quantitative race-nonspecific resistance in their genetic background. Abstract Race-specific qualitative resistance genes (R-genes) are conferring complete resistance in many pathosystems, but are frequently overcome by new virulent pathogen races. Once the deployed R-genes are overcome, a wide variation of quantitative disease resistance (QDR) can be observed in a set of previously race-specific, i.e., completely resistant genotypes—a phenomenon known as “vertifolia effect.” This race-nonspecific QDR is considered to be more durable in the long term, but provides merely a partial protection against pathogens. This simulation study aimed to detangle race-specific R-gene-mediated resistance of pending selection candidates and the QDR in their genetic background by employing different genomic selection strategies. True breeding values that reflected performance data for rust resistance in wheat were simulated, and used in a recurrent genomic selection based on several prediction models and training population designs. Using training populations that were devoid of race-specific R-genes was thereby pivotal for an efficient improvement of QDR in the long term. Marker-assisted preselection for the presence of R-genes followed by a genomic prediction for accumulating the many small to medium effect loci underlying QDR in the genetic background of race-specific resistant genotypes appeared furthermore to be a promising approach to select simultaneously for both types of resistance. The practical application of such a knowledge-driven genomic breeding strategy offers the opportunity to develop varieties with multiple layers of resistance, which have the potential to prevent intolerable crop losses under epidemic situations by displaying a high level of QDR even when race-specific R-genes have been overcome by evolving pathogen populations. Supplementary Information The online version contains supplementary material available at 10.1007/s00122-023-04312-2.


Figure S2
Average prediction accuracy in the simulated recurrent selection schemes without preselecting the sets of potential parents (Baseline strategy) for the genetic foreground comprising both race-specific and race-nonspecific disease resistance, i.e. when race-specific R-genes were considered to be effective in the training populations as well as in the progeny populations (solid lines; closed circles) as well as the genetic background comprising solely the race-nonspecific quantitative disease resistance, i.e. when race-specific R-genes would have been overcome by the pathogen and not be effective anymore in the progeny populations (dashed lines; open circles). It was assumed that the racespecific R-genes are still effective in the entire training population when fitting genomic best linear prediction models with a mixed training population of race-specific and race-nonspecific resistant genotypes (GBLUP ), which were compared with models including fixed effects for the most significant marker-trait associations that were either upweighted (WBLUP ) or unweighted (mWBLUP ) in the computation of the genomic estimated breeding values. The potential of training population devoid of race-specific resistant lines was furthermore tested by removing the respective lines based on mapped marker-trait associations (GBLUP ( ) ) or with a disease severity smaller than x = 10%, x = 20%, x = 30%, x = 40%, or x = 50% (GBLUP ( ) ). Result are shown for an initial resistance allele frequency of 0.2-0.3 (A+B), 0.3-0.4 (C+D), 0.4-0.5 (E+F), 0.5-0.6 (G+H) at the simulated R-genes in the founder population.

Figure S3
Average prediction accuracy in the simulated recurrent selection schemes when pre-selecting the sets of potential parents for the absence of race-specific R-gene mediated resistance (-R-gene strategy) for the genetic foreground comprising both race-specific and race-nonspecific disease resistance, i.e. when race-specific R-genes were considered to be effective in the training populations as well as in the progeny populations (solid lines; closed circles) as well as the genetic background comprising solely the race-nonspecific quantitative disease resistance, i.e. when race-specific R-genes would have been overcome by the pathogen and not be effective anymore in the progeny populations (dashed lines; open circles). It was assumed that the race-specific R-genes are still effective in the entire training population when fitting genomic best linear prediction models with a mixed training population of race-specific and race-nonspecific resistant genotypes (GBLUP ), which were compared with models including fixed effects for the most significant marker-trait associations that were either upweighted (WBLUP ) or unweighted (mWBLUP ) in the computation of the genomic estimated breeding values. The potential of training population devoid of race-specific resistant lines was furthermore tested by removing the respective lines based on mapped marker-trait associations (GBLUP ( ) ) or with a disease severity smaller than x = 10%, x = 20%, x = 30%, x = 40%, or x = 50% (GBLUP ( ) ). Result are shown for an initial resistance allele frequency of 0.2-0.3 (A+B), 0.3-0.4 (C+D), 0.4-0.5 (E+F), 0.5-0.6 (G+H) at the simulated R-genes in the founder population.

Figure S4
Average prediction accuracy in the simulated recurrent selection schemes when pre-selecting the sets of potential parents for the presence of race-specific R-gene mediated resistance resistance (-R-gene strategy) for the genetic foreground comprising both race-specific and race-nonspecific disease resistance, i.e. when race-specific R-genes were considered to be effective in the training populations as well as in the progeny populations (solid lines; closed circles) as well as the genetic background comprising solely the race-nonspecific quantitative disease resistance, i.e. when race-specific R-genes would have been overcome by the pathogen and not be effective anymore in the progeny populations (dashed lines; open circles). It was assumed that the race-specific R-genes are still effective in the entire training population when fitting genomic best linear prediction models with a mixed training population of race-specific and race-nonspecific resistant genotypes (GBLUP ), which were compared with models including fixed effects for the most significant marker-trait associations that were either upweighted (WBLUP ) or unweighted (mWBLUP ) in the computation of the genomic estimated breeding values. The potential of training population devoid of race-specific resistant lines was furthermore tested by removing the respective lines based on mapped marker-trait associations (GBLUP ( ) ) or with a disease severity smaller than x = 10%, x = 20%, x = 30%, x = 40%, or x = 50% (GBLUP ( ) ). Result are shown for an initial resistance allele frequency of 0.2-0.3 (A+B), 0.3-0.4 (C+D), 0.4-0.5 (E+F), 0.5-0.6 (G+H) at the simulated R-genes in the founder population.

Figure S5
Average disease severity in the simulated recurrent selection schemes for the genetic foreground comprising both race-specific and race-nonspecific disease resistance, i.e. when racespecific R-genes were considered to be effective in the training populations as well as in the progeny populations (solid lines; closed circles) as well as the genetic background comprising solely the racenonspecific quantitative disease resistance, i.e. when race-specific R-genes would have been overcome by the pathogen and not be effective anymore in the progeny populations (dashed lines; open circles). It was assumed that the race-specific R-genes are still effective in the entire training population when fitting genomic best linear prediction models with a mixed training population of race-specific and racenonspecific resistant genotypes (GBLUP ), which were compared with models including fixed effects for the most significant marker-trait associations that were either upweighted (WBLUP ) or unweighted (mWBLUP ) in the computation of the genomic estimated breeding values. The potential of training population devoid of race-specific resistant lines was furthermore tested by removing the respective lines based on mapped marker-trait associations (GBLUP ( ) ) or with a disease severity smaller than x = 10%, x = 20%, x = 30%, x = 40%, or x = 50% (GBLUP ( ) ). The best 20 crosses among all 400 parents within each selection cycle were selected based on the genomic estimated mid-parent values obtained by the different model-by-training population combinations (A-C; baseline strategy) or after a marker-assisted pre-selection for the absence (D-F; -R-gene strategy) or presence (G-I; + Rgene strategy) of race-specific R-gene mediated resistance among the potential parents. Result are shown for a resistance allele frequency of 0.3-0.4 at the simulated R-genes in the founder population.

Figure S6
Average disease severity in the simulated recurrent selection schemes for the genetic foreground comprising both race-specific and race-nonspecific disease resistance, i.e. when racespecific R-genes were considered to be effective in the training populations as well as in the progeny populations (solid lines; closed circles) as well as the genetic background comprising solely the racenonspecific quantitative disease resistance, i.e. when race-specific R-genes would have been overcome by the pathogen and not be effective anymore in the progeny populations (dashed lines; open circles). It was assumed that the race-specific R-genes are still effective in the entire training population when fitting genomic best linear prediction models with a mixed training population of race-specific and racenonspecific resistant genotypes (GBLUP ), which were compared with models including fixed effects for the most significant marker-trait associations that were either upweighted (WBLUP ) or unweighted (mWBLUP ) in the computation of the genomic estimated breeding values. The potential of training population devoid of race-specific resistant lines was furthermore tested by removing the respective lines based on mapped marker-trait associations (GBLUP ( ) ) or with a disease severity smaller than x = 10%, x = 20%, x = 30%, x = 40%, or x = 50% (GBLUP ( ) ). The best 20 crosses among all 400 parents within each selection cycle were selected based on the genomic estimated mid-parent values obtained by the different model-by-training population combinations (A-C; baseline strategy) or after a marker-assisted pre-selection for the absence (D-F; -R-gene strategy) or presence (G-I; + Rgene strategy) of race-specific R-gene mediated resistance among the potential parents. Result are shown for a resistance allele frequency of 0.4-0.5 at the simulated R-genes in the founder population.

Figure S7
Average disease severity in the simulated recurrent selection schemes for the genetic foreground comprising both race-specific and race-nonspecific disease resistance, i.e. when racespecific R-genes were considered to be effective in the training populations as well as in the progeny populations (solid lines; closed circles) as well as the genetic background comprising solely the racenonspecific quantitative disease resistance, i.e. when race-specific R-genes would have been overcome by the pathogen and not be effective anymore in the progeny populations (dashed lines; open circles). It was assumed that the race-specific R-genes are still effective in the entire training population when fitting genomic best linear prediction models with a mixed training population of race-specific and racenonspecific resistant genotypes (GBLUP ), which were compared with models including fixed effects for the most significant marker-trait associations that were either upweighted (WBLUP ) or unweighted (mWBLUP ) in the computation of the genomic estimated breeding values. The potential of training population devoid of race-specific resistant lines was furthermore tested by removing the respective lines based on mapped marker-trait associations (GBLUP ( ) ) or with a disease severity smaller than x = 10%, x = 20%, x = 30%, x = 40%, or x = 50% (GBLUP ( ) ). The best 20 crosses among all 400 parents within each selection cycle were selected based on the genomic estimated mid-parent values obtained by the different model-by-training population combinations (A-C; baseline strategy) or after a marker-assisted pre-selection for the absence (D-F; -R-gene strategy) or presence (G-I; + Rgene strategy) of race-specific R-gene mediated resistance among the potential parents. Result are shown for a resistance allele frequency of 0.5-0.6 at the simulated R-genes in the founder population.

Figure S9
Averaged proportion of race-specific resistant lines among the best 10-50% genomically selected lines by the different prediction model-by-training population combinations. It was assumed that the race-specific R-genes are still effective in the entire training population when fitting genomic best linear prediction models with a mixed training population of race-specific and race-nonspecific resistant genotypes (GBLUP ), which were compared with models including fixed effects for the most significant marker-trait associations, which were either upweighted (WBLUP ) or unweighted (mWBLUP ) in the computation of the genomic estimated breeding values. The potential of training population devoid of race-specific resistant lines was furthermore tested by removing the respective lines based on mapped marker-trait associations (GBLUP ( ) ) or with a disease severity smaller than x = 10%, x = 20%, x = 30%, x = 40%, or x = 50% (GBLUP ( ) ). Result are shown for a resistance allele frequency of 0.2-0.3 (A+B), 0.3-0.4 (C+D), 0.4-0.5 (E+F), 0.5-0.6 (G+H) at the simulated R-genes in the validation population.

Figure S10
Averaged percentage of correctly selected best race-nonspecific quantitative resistant (QDR) lines among the best 10-50% genomically selected lines by the different prediction model-bytraining population combinations. It was assumed that the race-specific R-genes are still effective in the entire training population when fitting genomic best linear prediction models with a mixed training population of race-specific and race-nonspecific resistant genotypes (GBLUP ), which were compared with models including fixed effects for the most significant marker-trait associations, which were either upweighted (WBLUP ) or unweighted (mWBLUP ) in the computation of the genomic estimated breeding values. The potential of training population devoid of race-specific resistant lines was furthermore tested by removing the respective lines based on mapped marker-trait associations (GBLUP ( ) ) or with a disease severity smaller than x = 10%, x = 20%, x = 30%, x = 40%, or x = 50% (GBLUP ( ) ). Result are shown for a resistance allele frequency of 0.2-0.3 (A+B), 0.3-0.4 (C+D), 0.4-0.5 (E+F), 0.5-0.6 (G+H) at the simulated R-genes in the validation population.

Figure S11
Frequency of the resistant allele R1 (A-D) and R2 (E-H) at the simulated race-specific R-genes as well as the frequency of race-specific resistant lines (I-L) in the recurrent selection without pre-selecting the sets of potential parents (Baseline strategy). Result are shown for a resistance allele frequency of 0.2-0.3 (A+E+I), 0.3-0.4 (B+F+J), 0.4-0.5 (C+G+K), and 0.5-0.6 (D+H+L) at the simulated R-genes in the founder population. It was assumed that the racespecific R-genes are still effective in the entire training population when fitting genomic best linear prediction models with a mixed training population of racespecific and race-nonspecific resistant genotypes (GBLUP ), which were compared with models including fixed effects for the most significant marker-trait associations, which were either upweighted (WBLUP ) or unweighted (mWBLUP ) in the computation of the genomic estimated breeding values. The potential of training population devoid of race-specific resistant lines was furthermore tested by removing the respective lines based on mapped marker-trait associations (GBLUP ( ) ) or with a disease severity smaller than x = 10%, x = 20%, x = 30%, x = 40%, or x = 50% (GBLUP ( ) ).

Figure S12
Frequency of the resistant allele R1 (A-D) and R2 (E-H) at the simulated race-specific R-genes as well as the frequency of race-specific resistant lines (I-L) in the recurrent selection when pre-selecting the sets of potential parents for the absence of race-specific R-gene mediated resistance (-R-gene strategy). Result are shown for a resistance allele frequency of 0.2-0.3 (A+E+I), 0.3-0.4 (B+F+J), 0.4-0.5 (C+G+K), and 0.5-0.6 (D+H+L) at the simulated R-genes in the founder population. It was assumed that the race-specific R-genes are still effective in the entire training population when fitting genomic best linear prediction models with a mixed training population of race-specific and race-nonspecific resistant genotypes (GBLUP ), which were compared with models including fixed effects for the most significant marker-trait associations, which were either upweighted (WBLUP ) or unweighted (mWBLUP ) in the computation of the genomic estimated breeding values. The potential of training population devoid of race-specific resistant lines was furthermore tested by removing the respective lines based on mapped marker-trait associations (GBLUP ( ) ) or with a disease severity smaller than x = 10%, x = 20%, x = 30%, x = 40%, or x = 50% (GBLUP ( ) ).

Figure S13
Frequency of the resistant allele R1 (A-D) and R2 (E-H) at the simulated race-specific R-genes as well as the frequency of race-specific resistant lines (I-L) in the recurrent selection when pre-selecting the sets of potential parents for the presence of race-specific R-gene mediated resistance (+ R-gene strategy). Result are shown for a resistance allele frequency of 0.2-0.3 (A+E+I), 0.3-0.4 (B+F+J), 0.4-0.5 (C+G+K), and 0.5-0.6 (D+H+L) at the simulated R-genes in the founder population. It was assumed that the race-specific R-genes are still effective in the entire training population when fitting genomic best linear prediction models with a mixed training population of race-specific and race-nonspecific resistant genotypes (GBLUP ), which were compared with models including fixed effects for the most significant marker-trait associations, which were either upweighted (WBLUP ) or unweighted (mWBLUP ) in the computation of the genomic estimated breeding values. The potential of training population devoid of race-specific resistant lines was furthermore tested by removing the respective lines based on mapped marker-trait associations (GBLUP ( ) ) or with a disease severity smaller than x = 10%, x = 20%, x = 30%, x = 40%, or x = 50% (GBLUP ( ) ).

Figure S14
Averaged percentage of most significant marker-trait association (MTA) detected on the same chromosome as the simulated race-specific resistance genes R1 and R2 (A+C) by genome-wide association mapping in the training populations as well as the average genetic distance between the marker-trait associations and the causal variants (B+D).

Figure S15
Average size of training populations (±standard error) devoid of race-specific resistant lines after removing the respective lines based on mapped marker-trait associations (GBLUPQDR(MTA)) or with a disease severity smaller than 10-50% (GBLUPQDR(CULL)). The training population size for the genomic best linear predictions (GBLUPMIX) with a mixed training population of race-specific and race-nonspecific resistant genotypes and for models that included fixed effect for the most significant marker-trait associations, which were either upweighted (WBLUPMIX) or unweighted (mWBLUPMIX) in the computation of the genomic estimated breeding values was always set to 800 lines.